M4oE: A Multi-Task Multi-Input Multi-Scale Mixture-of-Experts Method for Multi-Sensor Fusion Diagnosis

Rotating machinery fault diagnosis is essential for ensuring the reliability of industrial assets in IIoT-enabled manufacturing environments, where the high variability of operating conditions has driven the widespread adoption of multi-sensor fusion (MSF) to extract discriminative fault features. However, harsh IIoT environments frequently cause partial sensor failures or communication interruptions, under which conventional multi-sensor fusion systems often suffer significant performance degradation or even complete fusion failure. To address this issue, a Multi-task Multi-input Multi-scale Mixture-of-Experts (M4oE) framework is proposed, in which an independent diagnosis task is constructed for each sensor signal, ensuring that any individual sensor stream, representing the most extreme case of single-sensor availability, can independently perform diagnostic inference. First, a multi-scale mixture-of-experts feature extraction scheme is proposed, in which both the task-specific experts and each shared expert are implemented using the proposed Omni-scale Dilated Convolution Neural Network (OSD-CNN) architecture. Subsequently, task-specific features and shared features are integrated through multi-level feature fusion and fed into task-specific decoders to generate diagnostic results. Since the diagnostic outputs obtained from each sensor have the same identification framework and independent evidence sources, M4oE further employs Dempster-Shafer (D-S) decision-level fusion to enhance the reliability of the overall diagnostic system. Finally, comprehensive evaluations are conducted on three different types of rotating machinery datasets, including pumps, rolling mills, and bogies, to verify the accuracy, robustness, and scalability of the proposed M4oE framework.